{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gas Well Test Analysis : Isochronal Test\n",
    "\n",
    "> Divyanshu Vyas | Oil and Gas Data Science/Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = [48,12,15,12,17,12,18,12,72,100]\n",
    "\n",
    "pw = [1952,1761,1952,1694,1952,1510,1952,1320,1151,1952]\n",
    "\n",
    "q = [0,2.6,0,3.3,0,5.0,0,6.3,6.0,0]\n",
    "\n",
    "labels = ['S0','F1','S1','F2','S2','F3','S3','F4','EF','S_f']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10, 10, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(t),len(pw),len(q),len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Test':labels,'T(hrs)':t,'Pw':pw,'Q(MMscf/d)':q})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0</td>\n",
       "      <td>48</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761</td>\n",
       "      <td>2.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S1</td>\n",
       "      <td>15</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694</td>\n",
       "      <td>3.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S2</td>\n",
       "      <td>17</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S3</td>\n",
       "      <td>18</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S_f</td>\n",
       "      <td>100</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test  T(hrs)    Pw  Q(MMscf/d)\n",
       "0   S0      48  1952         0.0\n",
       "1   F1      12  1761         2.6\n",
       "2   S1      15  1952         0.0\n",
       "3   F2      12  1694         3.3\n",
       "4   S2      17  1952         0.0\n",
       "5   F3      12  1510         5.0\n",
       "6   S3      18  1952         0.0\n",
       "7   F4      12  1320         6.3\n",
       "8   EF      72  1151         6.0\n",
       "9  S_f     100  1952         0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pbar = 1952 #psia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Pbar2 - Pwf2'] = pbar**2 - df['Pw']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "      <th>Pbar2 - Pwf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S0</td>\n",
       "      <td>48</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761</td>\n",
       "      <td>2.6</td>\n",
       "      <td>709183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S1</td>\n",
       "      <td>15</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694</td>\n",
       "      <td>3.3</td>\n",
       "      <td>940668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S2</td>\n",
       "      <td>17</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1530204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S3</td>\n",
       "      <td>18</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2067904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2485503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S_f</td>\n",
       "      <td>100</td>\n",
       "      <td>1952</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test  T(hrs)    Pw  Q(MMscf/d)  Pbar2 - Pwf2\n",
       "0   S0      48  1952         0.0             0\n",
       "1   F1      12  1761         2.6        709183\n",
       "2   S1      15  1952         0.0             0\n",
       "3   F2      12  1694         3.3        940668\n",
       "4   S2      17  1952         0.0             0\n",
       "5   F3      12  1510         5.0       1530204\n",
       "6   S3      18  1952         0.0             0\n",
       "7   F4      12  1320         6.3       2067904\n",
       "8   EF      72  1151         6.0       2485503\n",
       "9  S_f     100  1952         0.0             0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3810087.91"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AOF conditions\n",
    "delP2_aof = pbar**2 - 14.7**2\n",
    "\n",
    "delP2_aof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_isotest = df[df['Q(MMscf/d)']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "      <th>Pbar2 - Pwf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761</td>\n",
       "      <td>2.6</td>\n",
       "      <td>709183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694</td>\n",
       "      <td>3.3</td>\n",
       "      <td>940668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1530204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2067904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2485503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test  T(hrs)    Pw  Q(MMscf/d)  Pbar2 - Pwf2\n",
       "1   F1      12  1761         2.6        709183\n",
       "3   F2      12  1694         3.3        940668\n",
       "5   F3      12  1510         5.0       1530204\n",
       "7   F4      12  1320         6.3       2067904\n",
       "8   EF      72  1151         6.0       2485503"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_isotest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Note- The Last test row is for Extended flow. \n",
    "#It will have a seperate line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x294b9de1d90>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(np.log(df_isotest['Q(MMscf/d)'][:-1]),np.log(df_isotest['Pbar2 - Pwf2'][:-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's model this Equation. \n",
    "iso = np.polyfit(np.log(df_isotest['Q(MMscf/d)'][:-1]),np.log(df_isotest['Pbar2 - Pwf2'][:-1]),deg=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.20218473, 12.31940829])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### $ log_e (P_{avg}^2 - P_{wf}^{2}) = 1.202*log_e(qg) + 12.31 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_isotest.reset_index(inplace=True,drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "      <th>Pbar2 - Pwf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761</td>\n",
       "      <td>2.6</td>\n",
       "      <td>709183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694</td>\n",
       "      <td>3.3</td>\n",
       "      <td>940668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1530204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2067904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2485503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test  T(hrs)    Pw  Q(MMscf/d)  Pbar2 - Pwf2\n",
       "0   F1      12  1761         2.6        709183\n",
       "1   F2      12  1694         3.3        940668\n",
       "2   F3      12  1510         5.0       1530204\n",
       "3   F4      12  1320         6.3       2067904\n",
       "4   EF      72  1151         6.0       2485503"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_isotest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.831946755407654"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso_q = df_isotest['Q(MMscf/d)'][:-1]\n",
    "iso_p2 = df_isotest['Pbar2 - Pwf2'][:-1]\n",
    "\n",
    "#1/n = 1.202\n",
    "#n = 1/1.202\n",
    "n = 1/1.202\n",
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Empirical Rule \n",
    "\n",
    "$ Q = C*(Pbar^2 - P_w^2)^n $\n",
    "\n",
    "$ 1/n * log(Q) + log(1/C) = log(Pbar^2 - P_w^2) $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.572290729504617"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Therefore Stabilized Model becomes - \n",
    "logc_s = np.log(2485503) - (1.202)*np.log(6)\n",
    "\n",
    "logc_s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stabilized (Empirical) Model for Isochronal test:-\n",
    "\n",
    "$ log(Pbar^2 - P_w^2) = 1.202*log(Q_g) + 12.5723 $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stab_iso(q):\n",
    "    \n",
    "    logp2 = 1.202*np.log(q) + 12.5723\n",
    "    \n",
    "    return np.exp(logp2)\n",
    "    \n",
    "    \n",
    "\n",
    "def stab_q(pw):\n",
    "    \n",
    "    logq = (np.log(pbar**2 - pw**2) - 12.5723)/1.202\n",
    "    \n",
    "    return np.exp(logq)\n",
    "\n",
    "\n",
    "aof_stab = stab_q(14.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-83-60681622bce7>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_isotest['Stablized : P2'] = df_isotest['Q(MMscf/d)'].apply(stab_iso)\n"
     ]
    }
   ],
   "source": [
    "df_isotest['Stablized : P2'] = df_isotest['Q(MMscf/d)'].apply(stab_iso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$P_{av}^2 - Pw^2 (Psi^2) - Log Scaled$')"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x825 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150);\n",
    "plt.style.use('seaborn')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "\n",
    "#Isochronal Test Points Model (Used to find slope)\n",
    "plt.scatter(np.log(df_isotest['Q(MMscf/d)'][:-1]),np.log(df_isotest['Pbar2 - Pwf2'][:-1]))\n",
    "plt.plot(np.log(df_isotest['Q(MMscf/d)'][:-1]),np.log(df_isotest['Pbar2 - Pwf2'][:-1]),label='Isochronal Test Points')\n",
    "\n",
    "x = np.log(df_isotest['Q(MMscf/d)'][:-1])\n",
    "y = 1.202*x + 12.31\n",
    "# plt.plot(x,y)\n",
    "\n",
    "\n",
    "xf = np.log(6)\n",
    "yf = np.log(2485503)\n",
    "\n",
    "plt.scatter(xf,yf)\n",
    "\n",
    "\n",
    "plt.plot(np.log(df_isotest['Q(MMscf/d)']),np.log(df_isotest['Stablized : P2']),label='Stabilized Model')\n",
    "\n",
    "\n",
    "# plt.axvline(x=np.log(aof_stab), label='AOF (log-Scaled)')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel('Qg(MMscf/d)-Log Scaled')\n",
    "plt.ylabel('$P_{av}^2 - Pw^2 (Psi^2) - Log Scaled$')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "      <th>Pbar2 - Pwf2</th>\n",
       "      <th>Stablized : P2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761</td>\n",
       "      <td>2.6</td>\n",
       "      <td>709183</td>\n",
       "      <td>9.096587e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694</td>\n",
       "      <td>3.3</td>\n",
       "      <td>940668</td>\n",
       "      <td>1.211530e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1530204</td>\n",
       "      <td>1.996377e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2067904</td>\n",
       "      <td>2.635651e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2485503</td>\n",
       "      <td>2.485526e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test  T(hrs)    Pw  Q(MMscf/d)  Pbar2 - Pwf2  Stablized : P2\n",
       "0   F1      12  1761         2.6        709183    9.096587e+05\n",
       "1   F2      12  1694         3.3        940668    1.211530e+06\n",
       "2   F3      12  1510         5.0       1530204    1.996377e+06\n",
       "3   F4      12  1320         6.3       2067904    2.635651e+06\n",
       "4   EF      72  1151         6.0       2485503    2.485526e+06"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_isotest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = {'Test':'AOF',\n",
    "      'T(hrs)':'-',\n",
    "      'Pw':14.7,\n",
    "      'Q(MMscf/d)':aof_stab,\n",
    "      'Pbar2 - Pwf2':pbar**2-14.7**2,\n",
    "      'Stablized : P2':stab_iso(aof_stab)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Test</th>\n",
       "      <th>T(hrs)</th>\n",
       "      <th>Pw</th>\n",
       "      <th>Q(MMscf/d)</th>\n",
       "      <th>Pbar2 - Pwf2</th>\n",
       "      <th>Stablized : P2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F1</td>\n",
       "      <td>12</td>\n",
       "      <td>1761.0</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>709183.00</td>\n",
       "      <td>9.096587e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F2</td>\n",
       "      <td>12</td>\n",
       "      <td>1694.0</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>940668.00</td>\n",
       "      <td>1.211530e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F3</td>\n",
       "      <td>12</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1530204.00</td>\n",
       "      <td>1.996377e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F4</td>\n",
       "      <td>12</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>2067904.00</td>\n",
       "      <td>2.635651e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EF</td>\n",
       "      <td>72</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2485503.00</td>\n",
       "      <td>2.485526e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AOF</td>\n",
       "      <td>-</td>\n",
       "      <td>14.7</td>\n",
       "      <td>8.560345</td>\n",
       "      <td>3810087.91</td>\n",
       "      <td>3.810088e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Test T(hrs)      Pw  Q(MMscf/d)  Pbar2 - Pwf2  Stablized : P2\n",
       "0   F1     12  1761.0    2.600000     709183.00    9.096587e+05\n",
       "1   F2     12  1694.0    3.300000     940668.00    1.211530e+06\n",
       "2   F3     12  1510.0    5.000000    1530204.00    1.996377e+06\n",
       "3   F4     12  1320.0    6.300000    2067904.00    2.635651e+06\n",
       "4   EF     72  1151.0    6.000000    2485503.00    2.485526e+06\n",
       "5  AOF      -    14.7    8.560345    3810087.91    3.810088e+06"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_isotest = df_isotest.append(new,ignore_index=True)\n",
    "\n",
    "df_isotest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Isochronal Test Results : AOF = 8.56 MMscf/D')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x825 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150);\n",
    "plt.style.use('seaborn')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "\n",
    "#Isochronal Test Points Model (Used to find slope)\n",
    "plt.scatter(np.log(df_isotest['Q(MMscf/d)'][:-2]),np.log(df_isotest['Pbar2 - Pwf2'][:-2]))\n",
    "plt.plot(np.log(df_isotest['Q(MMscf/d)'][:-2]),np.log(df_isotest['Pbar2 - Pwf2'][:-2]),label='Isochronal Test Points')\n",
    "\n",
    "x = np.log(df_isotest['Q(MMscf/d)'][:-1])\n",
    "y = 1.202*x + 12.31\n",
    "# plt.plot(x,y)\n",
    "\n",
    "\n",
    "xf = np.log(6)\n",
    "yf = np.log(2485503)\n",
    "\n",
    "plt.scatter(xf,yf)\n",
    "\n",
    "\n",
    "plt.plot(np.log(df_isotest['Q(MMscf/d)']),np.log(df_isotest['Stablized : P2']),label='Stabilized Model')\n",
    "\n",
    "\n",
    "plt.axvline(x=np.log(aof_stab), label='AOF (log-Scaled)')\n",
    "plt.axhline(y=np.log(3.810088e+06), label='$\\ delta P_{AOF}^2$(log-Scaled)')\n",
    "\n",
    "\n",
    "plt.xlim(0,2.5)\n",
    "plt.ylim(13.4,15.5)\n",
    "\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel('Qg(MMscf/d)-Log Scaled')\n",
    "plt.ylabel('$P_{av}^2 - Pw^2 (Psi^2) - Log Scaled$')\n",
    "\n",
    "plt.title('Isochronal Test Results : AOF = 8.56 MMscf/D')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.560344627144966"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aof_stab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Theoritical Method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_isotest['delP2/Qg'] = df_isotest['Pbar2 - Pwf2']/df_isotest['Q(MMscf/d)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_isotest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.regplot(df_isotest['Q(MMscf/d)'][:-1],df_isotest['delP2/Qg'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# theo = np.polyfit(df_isotest['Q(MMscf/d)'][:-1],df_isotest['delP2/Qg'][:-1],deg=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# theo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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